Graph Neural Networks (GNNs) present significant potential to revolutionize automated Test Case Prioritization (TCP) in Quality Assurance (QA) by effectively modeling intricate software-test relationships. This study evaluates the performance of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) against traditional prioritization methods, including random, coverage-based, and historical-data-based prioritization. Employing five publicly available software project datasets, results indicate that GNN-based methods, particularly GCN, demonstrate superior performance with an average APFD (Average Percentage Faults Detected) score of 84.2%, outperforming conventional approaches. Despite their effectiveness, GNN methods face substantial challenges, notably computational complexity, scalability issues, data availability and quality concerns, and limited interpretability. Practical adoption also demands sophisticated graph construction, rigorous hyperparameter tuning, and integration into existing QA workflows. The findings emphasize the necessity for strategic implementation and further research in hybrid modeling, incremental learning, and explainable AI to maximize the benefits of GNNs in TCP.

Graph Neural Networks (GNN), Test Case Prioritization (TCP), Quality Assurance (QA), Software Testing, Graph Convolutional Networks (GCN)

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